An Efficient Indian Sign Language Recognition System using Sift Descriptor
Jasmine Kaur1, C. Rama Krishna2
1Jasmine Kaur, Dept. of Computer Science & Engineering, NITTTR, Chandigarh, India
2C. Rama Krishna , Dept. of Computer Science & Engineering, NITTTR, Chandigarh, India
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1456-1461 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8124088619/2019©BEIESP | DOI: 10.35940/ijeat.F8124.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Communication is one of the basic requirements for living in the world. Deaf and dumb people convey through Sign Language but normal people have difficulty to understand their language. In order to provide a source of medium between normal and differently abled people, Sign Language Recognition System (SLR) is a solution . American Sign Language (ASL) has attracted many researchers’ attention but Indian Sign Language Recognition (ISLR) is significantly different from ASL due to different phonetic, grammar and hand movement. Designing a system for Indian Sign Language Recognition becomes a difficult task. ISLR system uses Indian Sign Language (ISL) dataset for recognition but suffers from problem of scaling, object orientation and lack of optimal feature set. In this paper to address these issues, Scale-Invariant Feature Transform (SIFT) as a descriptor is used. It extracts the features that train the Feed Forward Back Propagation Neural Network (FFBPNN) and optimize it using Artificial Bee Colony (ABC) according to the fitness function. The dataset has been collected for alphabet from the video by extracting frames and for numbers it has been created manually from deaf and dumb students of NGO “Sarthak”. It has been shown through simulation results that there has been significant improvement in accurately identifying alphabets and numbers with an average accuracy of 99.43%.
Keywords: ISLR, ABC, FFBPNN, SIFT, SLR, ISLR, ASL, ISL.